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Machine learning for iOS developers

Organized into two sections, the book's clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models'both pre-trained and user-built'...

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Bibliographic Details
Main Author: Mishra, Abhishek
Format: eBook
Language:English
Published: Hoboken, NJ John Wiley And Sons, Inc 2020
Subjects:
Machine Learning / Http://id.loc.gov/authorities/subjects/sh85079324
Computers / Fast
Computers / Http://id.loc.gov/authorities/subjects/sh85029552
Machine Learning / Fast
Ordinateurs
Apprentissage Automatique
Computers
Ios (electronic Resource) / Fast
Computers / Machine Theory / Bisacsh
Computers / Aat
Machine Learning
Ios (electronic Resource) / Http://id.loc.gov/authorities/names/n2010039603
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Table of Contents:
  • Correlation
  • Principal Component Analysis
  • Recursive Feature Elimination
  • Summary
  • Chapter 4 Implementing Machine Learning on Mobile Apps
  • Device-Based vs. Server-Based Approaches
  • Apple's Machine Learning Frameworks and Tools
  • Task-Level Frameworks
  • Model-Level Frameworks
  • Format Converters
  • Transfer Learning Tools
  • Third-Party Machine-Learning Frameworks and Tools
  • Summary
  • Part 2 Machine Learning with CoreML, CreateML, and TuriCreate
  • Chapter 5 Object Detection Using Pre-trained Models
  • What Is Object Detection?
  • Reinforcement Learning
  • Batch Learning
  • Incremental Learning
  • Instance-Based Learning
  • Model-Based Learning
  • Common Machine Learning Algorithms
  • Linear Regression
  • Support Vector Machines
  • Logistic Regression
  • Decision Trees
  • Artificial Neural Networks
  • Sources of Machine Learning Datasets
  • Scikit-learn Datasets
  • AWS Public Datasets
  • Kaggle.com Datasets
  • UCI Machine Learning Repository
  • Summary
  • Chapter 2 The Machine-Learning Approach
  • The Traditional Rule-Based Approach
  • A Machine-Learning System
  • Picking Input Features
  • Introduction
  • What Does This Book Cover?
  • Additional Resources
  • Reader Support for This Book
  • Part 1 Fundamentals of Machine Learning
  • Chapter 1 Introduction to Machine Learning
  • What Is Machine Learning?
  • Tools Commonly Used by Data Scientists
  • Common Terminology
  • Real-World Applications of Machine Learning
  • Types of Machine Learning Systems
  • Supervised Learning
  • Unsupervised Learning
  • Semisupervised Learning
  • Preparing the Training and Test Set
  • Picking a Machine-Learning Algorithm
  • Evaluating Model Performance
  • The Machine-Learning Process
  • Data Collection and Preprocessing
  • Preparation of Training, Test, and Validation Datasets
  • Model Building
  • Model Evaluation
  • Model Tuning
  • Model Deployment
  • Summary
  • Chapter 3 Data Exploration and Preprocessing
  • Data Preprocessing Techniques
  • Obtaining an Overview of the Data
  • Handling Missing Values
  • Creating New Features
  • Transforming Numeric Features
  • One-Hot Encoding Categorical Features
  • Selecting Training Features
  • A Brief Introduction to Artificial Neural Networks
  • Downloading the ResNet50 Model
  • Creating the iOS Project
  • Creating the User Interface
  • Updating Privacy Settings
  • Using the Resnet50 Model in the iOS Project
  • Summary
  • Chapter 6 Creating an Image Classifier with the Create ML App
  • Introduction to the Create ML App
  • Creating the Image Classification Model with the Create ML App
  • Creating the iOS Project
  • Creating the User Interface
  • Updating Privacy Settings
  • Using the Core ML Model in the iOS Project
  • Summary
  • Chapter 7 Creating a Tabular Classifier with Create ML

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